On distances, paths and connections for hyperspectral image segmentation

TL;DR

This paper introduces new regional connectivity concepts, $ ext{eta}$ and $ ext{eta}$ connections, to improve hyperspectral image segmentation by incorporating regional information beyond local $ ext{lambda}$-flat zones.

Contribution

It proposes novel algorithms for segmentation that integrate $ ext{eta}$-bounded regions and $ ext{mu}$-geodesic balls, enhancing control over amplitude variations and class size.

Findings

Effective segmentation of hyperspectral images demonstrated

Algorithms based on queues and ordered seed selection show promising results

Regional information improves segmentation quality

Abstract

The present paper introduces the $\eta$ and {\eta} connections in order to add regional information on $\lambda$-flat zones, which only take into account a local information. A top-down approach is considered. First $\lambda$-flat zones are built in a way leading to a sub-segmentation. Then a finer segmentation is obtained by computing $\eta$-bounded regions and $\mu$-geodesic balls inside the $\lambda$-flat zones. The proposed algorithms for the construction of new partitions are based on queues with an ordered selection of seeds using the cumulative distance. $\eta$-bounded regions offers a control on the variations of amplitude in the class from a point, called center, and $\mu$-geodesic balls controls the "size" of the class. These results are applied to hyperspectral images.